Reviewerly/exHarmony huggingface dataset:
Hybrid quantum–classical convolutional neural networks with privacy quantum computing

Abstract Machine learning algorithms help us discover knowledge from big data. Data used for training or prediction often contain private information about users. Discovering knowledge while protecting data or user privacy is the way machine learning is expected, especially in the cloud environment. Quantum machine learning is a kind of machine learning that realizes parallel acceleration by quantum superposition. Quantum computing power for quantum machine learning is typically provided by quantum cloud computing services. Existing quantum machine learning algorithms hardly consider privacy protection. This paper presents an encryption method for image data which can effectively protect the input data privacy in hybrid quantum–classical convolutional neural networks algorithm. The user’s original image data is first encrypted, and then sent to the quantum cloud to calculate the image convolution. By doing so, the feature map of the ciphertext image is obtained by the user. The result obtained by decrypting the feature map is the same as that obtained by using the original image as the input of convolution calculation. Experiments show that our privacy protection scheme can protect the privacy of input image data in the hybrid quantum–classical neural networks algorithm, but does not affect the accuracy of the algorithm. In addition to image encryption and feature map decryption, the proposed scheme does not bring additional computational complexity.
Security, privacy, and trust management in DNA computing

DNA computing is an emerging field that aims at enabling more efficient data storage and processing. One principle of DNA computing is to encode some information (e.g., image, video, programming scripts) into a digital DNA-like sequence and then synthesize the corresponding DNA molecule. Synthesizing this molecule using digital or real human genomic fragments theoretically opens the possibility for privacy attacks, which have been demonstrated on a large array of human genomic data. These privacy attacks aim at breaching the privacy of DNA samples, allowing an attacker to discover privacy-critical information from the partial or complete DNA information of an individual. In the context of DNA computing, novel privacy attacks will certainly emerge and could consist in discovering a part of a particular script or video that is privacy-critical. It is therefore important to consider whether privacy attacks and defense mechanisms can be used when manipulating genomic data. First, this chapter provides the background about genomic data, and its modern generation and processing. It then provides a survey on known genomic privacy attacks, and presents the privacy-enhancing technologies that have been designed to protect genomic data. Later, this chapter also introduces the current trust management methods one can rely on to further secure DNA storage and processing methods, before discussing how DNA computing currently relates to those attacks and privacy-preserving technologies. Finally, this chapter presents future research avenues.
Accurate Automatic Camera Calibration on Low-Quality CCTV Traffic Video Streams

This paper focuses on extracting vehicle trajectory from low-quality, uncalibrated traffic cameras via fully automatic camera calibration and homography transformations. We analyze video streams from 511 traffic surveillance cameras in varying areas and arbitrary relative locations and orientations to roads. We propose a novel automatic calibration methodology that is used to transform the video streams to a top-down perspective from a minute of the video stream. Using this perspective, we can extract both lane changes and vehicle speed. Our automatic camera calibration algorithm is inspired by two vanishing point based automatic camera calibration methods. We implement more accurate and robust methods for the detection of both vanishing points. We show that YOLOv4 combined with DeepSORT is 12.5 times faster than the leading vehicle detection model and achieves a 7.81% higher average precision and 6.04% higher mean average precision in low quality traffic surveillance cameras. We use this model for a more robust estimation of the first vanishing point and more robust object tracking. Furthermore, we propose a fast, novel “guess and check” algorithm for the detection of the second vanishing point to ensure accurate second vanishing point detection. We show that our camera calibration algorithm produces fewer inaccuracies than the state-of-the-art automatic camera calibration methodology in [1] through qualitative results.
Automated Essay Scoring Using Machine Learning

Essays are frequently employed in the educational system to gauge students' comprehension of particular subjects. However, marking essays requires a lot of time and work and could be prejudiced. In order to save time, lessen human effort, and eliminate biased scoring, automated essay scoring tries to automate scoring. Due to its lack of transparency, limited language support, and requirement for tagged data for the target prompt, which is not always available, AES is still not frequently utilized. This study's goal is to examine automated essay scoring methods. The PRISMA Flow Diagram is used in this study to conduct a systematic literature review. Studies that were released between 2016 and 2021 were found. Information pertinent to the research topics is taken from these studies and then processed to provide a response. Datasets, methods, and models are found in the publications. The performance score of models utilizing the same dataset is then used to compare them. According to the study, AES uses feature engineering and deep learning as its two core methodologies. More scholars are currently researching the deep-learning methodology. CNN, LSTM, and BERT are a few examples of neural network models used in the deep learning method. Most studies use the average QWK and the ASAP dataset as performance metrics. SBLSTMA (Siamese Bidirectional LSTM Neural Network Architecture) and BERT + handcrafted-features, both with 0.801 average QWK, are the models with the highest performance score on the ASAP datasets.
An Asynchronously Alternative Stochastic Gradient Descent Algorithm for Efficiently Parallel Latent Feature Analysis on Shared-Memory

A latent feature analysis (LFA) model is highly efficient in performing representation learning to high-dimensional incomplete (HDI) data like user-item interactions data from a recommender system. Stochastic gradient descent (SGD) is frequently adopted as the learning algorithm by an LFA model owing to its low computational complexity. However, a standard SGD algorithm is in nature a serial algorithm, which affects the resultant LFA model's scalability on mass HDI data. On the other hand, existing parallel SGD algorithms commonly suffer from low speedup when building an LFA model due to their frequent synchronizations during the training process. Motivated by this discovery, this paper proposes an Asynchronously Alternative Stochastic Gradient Descent (A <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> SGD) algorithm to achieve an efficiently parallelized LFA model on shared-memory with two fold-ideas: a) adopting the principle of an alternative stochastic gradient descent algorithm to decouple the LFA process, thereby achieving two parallelizable subtasks with minimal learning information loss; b) designing a novel parallelization scheme by eliminating synchronizations from both the subtask and the thread perspectives, i.e., both sub-tasks are taken simultaneously, and their affiliated threads also executes without synchronizations. Rigorously theoretical convergence proof illustrates that the newly-proposed parallelization scheme guarantees the convergence of a resultant LFA model. Detailed experimental results on four real HDI datasets indicate that an A <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> SGD-based LFA model outperforms several state-of-the-art parallel SGD-based LFA models in terms of both missing data estimation accuracy and parallelization speedup.
Time-Series Analysis of Cryptocurrency Price: Bitcoin as a Case Study

Bitcoin (BTC) is a distributed virtual paradigm that uses a peer-to-peer network to provide a means of digital money. Bitcoin pricing was changing monthly, and the BTC price observations have been collected since October 2013. In this paper, we propose a neural network-based autoregressive predictive model to forecast the monthly pricing of cryptocurrency bitcoin technology based on 100 historical observations for the bitcoin prices from Oct-2013 to Sep-2021 (in us dollars). Specifically, the proposed scheme uses a nonlinear autoregressive neural network with external input (NARX) by detaining the maximum regression coefficient corresponding to the most prediction accuracy and the least normalized prediction error. The simulation results showed that the highest prediction accuracy for the identified cryptocurrency, bitcoin pricing is 99.1%. The subsequent perdition model was effectively used to anticipate the evolution of forthcoming 12-month data records for the n cryptocurrency bitcoin prices from Oct-2021 to Sep-2022. The forecast values reveal a very slow, linearly developing tendency in the prices of cryptocurrency bitcoin released monthly over the past ten years' records for the global cryptocurrency bitcoin pricing time series.
New Student Prediction Using Algorithm Naive Bayes And Regression Analysis In Universitas Potensi Utama

Universitas Potensi Utama has various study programs, and also has various facilities to support student learning activities. However, the main obstacle in higher education is the uncertainty of the interest of students who register, so that sometimes the facilities are inadequate. To overcome these problems, there must be activities to predict prospective new student applicants to improve facilities and interest of prospective students to choose to study at the Universitas Potensi Utama. Forecasting is the most important thing that must be applied to a company. With this forecasting, companies can see the opportunities that exist to generate sales predictions in the future based on the results of past sales data. Naive Bayes is a classification using probability and statistical methods, the Naive Bayes algorithm can be used in scientific fields, one of which is predicting future opportunities based on previous experience. Linear regression method can be used for forecasting with the assumption that the correlation between variables will continue in the future. Linear Regression is a Regression Method where the resulting equation is linear. Based on the resulting equation, predictions can be calculated by entering the values of the predictor variables in the equation. Based on this process, the predictive value of the response variable can be generated. In this study, the author explains more about how to determine new student predictions using a combination of the two algorithms where the Naïve Bayes algorithm is used to state which study programs have a lot of interest in the coming year, and the Linear Regression algorithm is used to show which study programs are most in demand based on the number of students who registered in the previous year, In the description that has been stated above, the author makes a paper with the title: “Predictions of New Student Registration Using the Naive Bayes Algorithm and Regression Analysis at the Universitas Potensi Utama”.
Formally verifying security protocols built on watermarking and jamming

Physical layer security mechanisms use primitives that exploit physical properties of the communication channel to protect data. Protecting communications at the physical layer offers some advantages, e.g., in terms of reduced computations, since complex cryptographic procedures are not executed, However, these mechanisms lack a formal specification that prevent protocols and applications that use them from being verified and compared with those based on cyptography. Here we start filling this gap by providing an axiomatization of key physical layer security primitives and proposing a variant of the Dolev–Yao attacker model that takes them into account. We show that our formalization enables applying existing automatic tools for verifying security of protocols. Then, we show that these primitives are a valuable alternative and effective complement to cryptography, because they ensure confidentiality and integrity but require a lower energy consumption and often they also reduce transmission time. Finally, we characterize the specific application domains and network features that make adopting these security mechanisms particularly profitable with respect to the AES cypher.
Goal model convergence and conflict detection for crossover services

As a new form of service model, crossover services aim to aggregate service resources across multiple domains to meet the complex needs of users and provide value-added services. It has received extensive attention from industry and academia due to its advantages in promoting enterprise services innovation. Crossover services span various business domains intending to meet diverse and continuously changing user requirements across multiple domains. Requirements engineering for crossover services must model user goals in various domains and converge them deeply to drive the subsequent service realization. Due to the domain heterogeneity, however, conflicts may frequently arise after the convergence of goals. Towards this issue, we propose a goal decomposition path-based method to support goal convergence of multiple domains and a computation tree logic-based method to detect conflicts between goals in the converged goal model. We evaluate the proposed method using several real cases from the literature and industry and design a controlled experiment to further assess the method’s performance. Experimental results show the effectiveness of our proposed method.
A New Speech Encoder Based on Dynamic Framing Approach

Latent information is difficult to get from the text in speech synthesis.Studies show that features from speech can get more information to help text encoding.In the field of speech encoding, a lot of work has been conducted on two aspects.The first aspect is to encode speech frame by frame.The second aspect is to encode the whole speech to a vector.But the scale in these aspects is fixed.So, encoding speech with an adjustable scale for more latent information is worthy of investigation.But current alignment approaches only support frame-by-frame encoding and speech-to-vector encoding.It remains a challenge to propose a new alignment approach to support adjustable scale speech encoding.This paper presents the dynamic speech encoder with a new alignment approach in conjunction with frame-by-frame encoding and speech-to-vector encoding.The speech feature from our model achieves three functions.First, the speech feature can reconstruct the origin speech while the length of the speech feature is equal to the text length.Second, our model can get text embedding from speech, and the encoded speech feature is similar to the text embedding result.Finally, it can transfer the style of synthesis speech and make it more similar to the given reference speech.
Robust Multi-Watermarking Algorithm for Medical Images Based on GoogLeNet and Henon Map

The field of medical images has been rapidly evolving since the advent of the digital medical information era.However, medical data is susceptible to leaks and hacks during transmission.This paper proposed a robust multi-watermarking algorithm for medical images based on GoogLeNet transfer learning to protect the privacy of patient data during transmission and storage, as well as to increase the resistance to geometric attacks and the capacity of embedded watermarks of watermarking algorithms.First, a pre-trained GoogLeNet network is used in this paper, based on which the parameters of several previous layers of the network are fixed and the network is fine-tuned for the constructed medical dataset, so that the pre-trained network can further learn the deep convolutional features in the medical dataset, and then the trained network is used to extract the stable feature vectors of medical images.Then, a two-dimensional Henon chaos encryption technique, which is more sensitive to initial values, is used to encrypt multiple different types of watermarked private information.Finally, the feature vector of the image is logically operated with the encrypted multiple watermark information, and the obtained key is stored in a third party, thus achieving zero watermark embedding and blind extraction.The experimental results confirm the robustness of the algorithm from the perspective of multiple types of watermarks, while also demonstrating the successful embedding of multiple watermarks for medical images, and show that the algorithm is more resistant to geometric attacks than some conventional watermarking algorithms.
Exploring Word2vec Embedding for Sentiment Analysis of Bangla Raw and Romanized Text

In this era of rapid technological advancement, every individual's daily life has become a routine of sharing their perspectives, opinions, emotions, and experiences through social networking sites and platforms on the Internet. These viewpoints can be used to establish strategies that can improve efficiency in a variety of areas such as business, politics, research, and analysis. Sentiment analysis (SA) is used in natural language processing (NLP) to automatically monitor, analyze, and categorize individuals' thoughts and opinions in order to acquire a sense of general sentiment. To date, a significant amount of research has been conducted on SA of English language with remarkable successes. Unfortunately, there has been relatively insufficient research in the field of SA with the Bangla language. Despite the fact that romanized Bangla has gained in popularity among Bangla speakers as a result of the recent surge in social networks, there is even less research on romanized Bangla text. Therefore, this research has concentrated on the analysis of sentiment for both Bangla raw and romanized texts. In this study, a corpus of romanized Bangla texts has been constructed from a raw Bangla sentiment corpus. Furthermore, both of these corpora have been tested for SA using the deep recurrent neural network with continuous bag of words and skip-gram word2vec word embeddings for both binary and multi-label classifications. Finally, this study concludes with the comparative results and analysis of SA of both forms of Bangla texts, where SA of romanized Bangla texts outperforms its raw form.
Sentiment Analysis of COVID-19 Tweets Using TextBlob and Machine Learning Classifiers

Social media are the influential Internet community for general netizens to disseminate information, views, and opinions extensively. Recently, the research on Sentiment analysis has intensified due to the vast amount of data obtained from these numerous social networking platforms. During this COVID-19 pandemic, one of the great social networking sites viz. Twitter has experienced a significant increase in online posts and reviews concerning COVID-19 related tweets. In the view of Twitter, the spread of news concerning coronavirus variants has impacted many aspects of the public by providing messages around different issues and opinions to its potential users. In this study, we have used Twitter as a source of data for searching the index keywords and hashtag versions for the terms like “Pandemic”, “Coronavirus”, “COVID-19”, “SARS-CoV-2”, and “Omicron”, and we have examined the psychological and emotional impact it had on the public. This paper provides analytical information about people’s perceptions of coronavirus (tweets were collected from March 2020 to December 2021). With those tweets, sentiment analysis was performed using the TextBlob text processing python module to acquire the people’s subjective data (opinions and feelings) polarity concerning the effects of coronavirus. Furthermore, for effective text classification, we have applied classification methodologies like Support Vector Machine (SVM), K Nearest Neighbor (KNN), Logistic Regression (LR), and Decision Tree (DT). The SVM concerning feature extraction produced the results with 99.49% accuracy. This study assists the government organizations in attaining COVID-19 insights by implying public mental health sentiments in social networks.
Aesthetic Attribute Assessment of Images Numerically on Mixed Multi-attribute Datasets

With the continuous development of social software and multimedia technology, images have become a kind of important carrier for spreading information and socializing. How to evaluate an image comprehensively has become the focus of recent researches. The traditional image aesthetic assessment methods often adopt single numerical overall assessment scores, which has certain subjectivity and can no longer meet the higher aesthetic requirements. In this article, we construct an new image attribute dataset called aesthetic mixed dataset with attributes (AMD-A) and design external attribute features for fusion. Besides, we propose an efficient method for image aesthetic attribute assessment on mixed multi-attribute dataset and construct a multitasking network architecture by using the EfficientNet-B0 as the backbone network. Our model can achieve aesthetic classification, overall scoring, and attribute scoring. In each sub-network, we improve the feature extraction through ECA channel attention module. As for the final overall scoring, we adopt the idea of the teacher-student network and use the classification sub-network to guide the aesthetic overall fine-grain regression. Experimental results, using the MindSpore, show that our proposed method can effectively improve the performance of the aesthetic overall and attribute assessment.
Effects of Science, Technology, and Innovation Official Development Assistance on Innovative Capacity in Developing Countries

This study aims to empirically investigate the effect of Science, Technology, and Innovation Official Development Assistance (STI ODA) on the innovative capacity of developing countries. Particularly, this study attempted to examine the moderating effects of R&amp;D investment and its effect on innovative capacity. To do this, a panel fixed model analysis was carried out with ODA and macroeconomic data on 84 developing countries from 2002 to 2018. The findings indicated that STI ODA was found to have no direct positive effect on innovative capacity. However, it had a positive effect on innovative capacity when the moderating effect of R&amp;D investment was significant and when the proportion of R&amp;D investment increased. The findings of this study serve as a guide for policymakers in terms of having better understanding of the relationship between STI ODA, R&amp;D investment, and innovative capacity. Hence, policy makers and practitioners are able to design a good policy to be adopted such that absorptive capacity should be prioritized for STI ODA to be effective in helping developing countries to escape poverty and achieve sustainable development goals. To the best of the authors’ knowledge, this paper is the first of its kind to analyze the moderating effect of R&amp;D investment on the relationship between STI ODA and innovative capacity in a developing country context.