Reviewerly/exHarmony huggingface dataset:
The Speech Labeling and Modeling Toolkit (SLMTK) Version 1.0

This paper introduces the Speech Labeling and Modeling Toolkit version 1.0 (SLMTK 1.0), which facilitates automatic labeling of text and speech for constructing text-to-speech (TTS) systems and speech analysis. The SLMTK 1.0 supports mixed Mandarin-English speech and the associated texts. The following seven steps then process the inputs: 1) text analysis, 2) acoustic feature extraction, 3) linguistic-speech alignment, 4) integration of syllable-based linguistic and prosodic-acoustic features, 5) prosody labeling, 6) construction of prosody generation model, and 7) construction of acoustic models for speech synthesis. The outputs of the seven steps are, respectively, 1) linguistic labels, 2) acoustic features, 3) linguistic-speech alignment, 4) syllable-based linguistic and prosodic-acoustic features, 5) prosody tags, 6) prosody generation model, and 7) acoustic models for speech synthesis. The SLMTK 1.0 has been applied to constructing personalized TTS systems for augmentative and alternative communication. In addition, the toolkit has also been applied to phonetic and prosodic labeling of L2 Mandarin speech to facilitate prosody analysis studies. The SLMTK 1.0 is available at https://slmtk.ce.ntpu.edu.tw for non-commercial use and welcomes all parties to enrich the functions of the SLMTK.
Enhancing Background Luminance for Colorectal Cancer H and E Stained Images using Modified Reinhard Technique

With the advent of AI and Machine learning based learning, the overall process of cancer diagnosis became much smoother and faster through automated techniques. Because of the presence of artefacts that cause color changes in H&E stained histopathology images, color normalization is an important pre-processing step for cancer identification. However, the existing color normalization methods suffers from two major issues: Loss of information that leads to poor background luminance and huge computational complexity. To address this issue, we developed a modified Reinhard approach for color normalizing on the CRC dataset in order to improve the background luminance of H&E stained colorectal cancer histopathology photographs. Our proposed algorithm not only mitigate the limitations of the previous reinhard method but statistically satisfy all four hypothesis of the color normalization by incorporating a global feature along with local one. Our algorithm's performance was also compared to that of other current color normalization algorithms, and it was shown to be superior in both quantitative and qualitative terms.
DASCDC: Dual Attention-based Sparse Common Deep Clustering Method

Graph convolutional network (GCN) has been successfully applied in deep clustering on the high dimensional dataset, which is usually processed by extracting the feature and structure information. However, it lacks an effective fusion mechanism of common information between the feature and structure information and does not perform well on the sparse dataset. Therefore, we propose a dual attention-based sparse common deep clustering method(DASCDC). Firstly, we advance a sparse feature representation method with an adversarial loss function to solve the data sparsity problem and improve the discriminator’s performance. Then, an information interaction operator is designed to deeply mine the common information and enhance the information fusion capability. Finally, considering the different contribution degrees of various neighbour nodes and information, we promote a dual self-attention mechanism from the node and model levels. Experiment results indicate that the proposed DASCDC algorithm overperforms other state-of-art deep clustering algorithms on non-graph and graph datasets with high dimension and sparsity.
Multi-dimensional alignment via Variational Autoencoders for Generalized Zero-Shot and Few-Shot Learning

Generalized zero-shot learning (GZSL) aims to solve the category recognition tasks for unseen categories under the setting that training samples only contain seen classes while unseen classes are not available. This research is vital as there are always existing new categories and large amounts of unlabeled data in realistic scenarios. Previous work for GZSL usually maps the visual information of the visible classes and the semantic description of the invisible classes into the identical embedding space to bridge the gap between the disjointed visible and invisible classes, while ignoring the intrinsic features of visual images, which are sufficiently discriminative to classify themselves. To better use discriminative information from visual classes for GZSL, we propose the n-CADA-VAE. In our approach, we map the visual feature of seen classes to a high-dimensional distribution while mapping the semantic description of unseen classes to a low-dimensional distribution under the same latent embedding space, thus projecting information of different modalities to corresponding space positions more accurately. We conducted extensive experiments on four benchmark datasets (CUB, SUN, AWA1, and AWA2). The results show our model’s superior performance in generalized zero-shot as well as few-shot learning.
Wind Power Forecasting using VARMAX

Wind power being one of the sustainable, renewable energy sources still must compete with the conventional sources of energy on a cost basis. With the use of IIOT data from the turbine, its maintenance requirement, expected production, failure rate and how long it will last can be determined. This paper explores the usability of a turbine's IIOT time series data collected over a period of one year to forecast the future power generation that can be expected for next three months. Ten features were taken into consideration after determining their causal effect on production using Granger causality test. Since some features can affect the production of turbine independently, Vector Auto Regression with Exogenous variable model is used for forecasting. The MAPE rate of 17% was achieved in the forecast of next three months.
Student eHandbook Packet: A Mobile Application for Bulacan State University

This mixed-method research utilizing descriptive-developmental design is about designing and evaluating Student eHandbook Packet Mobile Application employs an agile-scrum methodology for mobile app development. Consultative meetings and Focus Group Discussions were held to obtain data from 16 BulSU administrative council members as the alpha evaluators, 10 IT experts as the beta evaluators, and 43 student leaders as the gamma evaluators chosen using purposive sampling. Results show that the mobile app is excellent in terms of functional suitability ( <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$M=4.72$</tex> ), performance efficiency ( <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$M=4.78$</tex> ), compatibility ( <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$M=4.72$</tex> ), usability ( <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$M=4.80$</tex> ), reliability ( <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$M=4.67$</tex> ), security ( <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$M=4.58$</tex> ), maintainability ( <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$M=4.73$</tex> ), portability ( <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$M=4.77$</tex> ) recording a grand mean of 4.72 interpreted as excellent. This means that the application satisfies both software quality standards and end-user requirements. Word cloud analysis suggests that the application is useful to students. Thus, it is ready for adoption. Along with its implementation, it is recommended to conduct an impact analysis of the effectiveness of using the student eHandbook mobile application.
Improved IoT for Health Behaviour System Based on Machine Learning Model

Machine learning can assist in the difficult work of extracting meaningful information from the seemingly useless data produced by IoT devices (ML). The careful deployment of hybrid technologies has reaped benefits for a wide range of institutions, including businesses, governments, schools, and hospitals. The Internet of Things (IoT) may use machine learning (ML) to identify previously hidden patterns in large volumes of data in order to create accurate forecasts and recommendations. The Internet of Things (IoT) and machine learning (ML) are being applied in the field of medicine in order to automate the process of creating medical records, predicting illness diagnoses, and, most importantly, continuously monitoring patients. On different datasets, different machine learning algorithms achieve differing degrees of success. The numerous predictions may or may not end up having an effect on the eventual result. The degree to which the results differ from one another plays a crucial part in the therapeutic decision-making process. The healthcare industry relies significantly on a variety of ML algorithms in order to successfully manage the data generated by IoT devices. In this post, we are going to talk about how popular machine learning techniques can be used in the field of medicine for categorization and prediction purposes. The objective of this study is to provide evidence that utilizing a more sophisticated ML model for the analysis of IoT health data is beneficial. After a substantial amount of time spent on the matter, we came to the realization that a number of well-known ML prediction algorithms have significant weaknesses. The type of Internet of Things dataset that is being utilized will determine the technique that will be most effective when attempting to anticipate vital health data. The paper demonstrates a number of the ways in which the Internet of Things and machine learning have affected the delivery of healthcare in a variety of settings.
Deep Learning Based Facemask Detection

The Covid-19 pandemic created a massive impact on various sectors across the globe. Nearly 400 million people have been affected by Covid-19 as of January 2022. Although vaccines have been developed, only 49.8% of world population have been vaccinated. The W.H.O has advised the public to maintain social distance in crowded places and wear well fitted mask to impede the spread of corona virus. It has been made mandatory by most countries to wear mask in public places, human monitoring continuously is impossible hence we deploy Deep learning model to implement the same. In this paper we have trained mobilenetV2 architecture for facemask detection using custom dataset. The accuracy of the model in real time is 99.99%
Real-time Visual Detection and Tracking is Implemented in a Clustered Environment using an Adaptive Kernel-Supported Correlation Filter Algorithm

Following moving articles alongside their development through video groupings are perhaps of the most essential and most vital undertaking in PC vision. This fills in as the establishment for various more significant level mechanized applications in various spaces, including observation, expanded reality and movement catch in moving item discovery. Object following is key component of an IVS framework which can additionally be demonstrated for some dubious movement identification frameworks. There are numerous approaches and proposed algorithms for object tracking, but the article proposed Scale Adaptive Kernel Support Correlation Filter Algorithm (SKSCF), which is the basis for the implementation of IVS in this paper. It also derives an equivalent formulation of an SVM model with the circulant matrix expression and presents an effective alternating optimization method for visual tracking. The proposed work characterized to meet following goals: to make a video grouping for moving item following; to plan an exploratory set ready for moving item discovery; and, to plan and carry out moving item following calculation, the proposed calculation was carried out on a caught video succession. Object was identified first as per the picture info, and afterward followed in ensuing casings. The exploratory execution could play out the article following without missing any edge and could effectively overlay bouncing box. It could effectively create a picture grouping after the total execution of Mean Shift Flowchart. The presentation of calculation was checked by effectively following the client characterized object at any climate and playing out the overlay capability in the recognized article.
A Revised Converter Paradigm Designed for Spam Message Exposure

Within this paper, we point to consider the plausibility of recognizing spams in mobile phone sms messages by recommending an improved Converter method. This method is planned for recognizing spams in SMS messages. We use “Spam Collection v.1 dataset” as well as “UtkMl's Twitter Spam Location Competition” dataset to evaluate our proposed spam Detector, with a number of well-known machine learning classifiers and cutting-edge SMS spam detection techniques serving as the benchmarks. In our paper, we use networks such by way of long short term memory (LSTM), bi-directional LSTM, and encoder-decoder LSTM models which are recurrent neural networks. Our investigations on SMS spam detection demonstrate that the proposed improved spam Converter outperforms all other alternatives regarding accuracy, F1-Score and recall. Additionally, the suggested model performs well on UtkMl's Twitter dataset, suggesting a favorable chance of applying model to other similar issues.
Analysis and Design of Self-service Local Water Company (LWC) using Vernam Cipher Cryptography Algorithm

The main factors affecting the performance of Local Water Company (LWC) when managing consumable water distribution in Indonesia are non-revenue water, less water usage effectiveness, less efficiency of billing records and customer complaints about services not becoming available for up to 24 hours. The factor happens because the process is still done manually. So errors and fraud are often found. This research aims to provide a solution by proposing the design of an LWC recording and billing system with a practical and safe prepaid Self-Service method. The prepaid Self-Service process is divided into two main functions. First, the real-time calculation function is designed to solve the efficiency problem in recording water usage. Second, the self-payment token's process is designed to resolve data processing and bill payment constraints. It generated tokens for self-payment token functions built using the Vernam Cipher Cryptographic Algorithm. An Android platform with an Arduino IDE is used in this system. A token will be sent to other devices through Bluetooth serial communication. The results were successfully performed using the Vernam Cipher Cryptographic Algorithm for the self-payment token function. The encryption token consisting of 48 characters can be automatically transferred to other devices using Bluetooth serial communication. The encryption process takes about 0.34 seconds, and the decryption takes about 0.20 seconds.
Automatic Clustering for Selection of Optimal Number of Clusters by K-Means Integrated with Enhanced Firefly Algorithms

Unsupervised machine learning includes data clustering, which discovers and comprehends underlying data patterns as well as classifies objects within a dataset based on similarity metrics such as Davies-Bouldin (DB) and CompactSeparatedValues (CSV) (CS). The most well-known and powerful partitional clustering algorithm is the K-means clustering algorithm, which is one of the top ten most frequently used data mining algorithms. We have combined the firefly metaheuristic optimization algorithm with K-means clustering to enhance the K-means for automatic clustering in our paper. The experimental results show that the hybridized algorithm KFA outperforms the k-means algorithm than PSO, KPSO, and K-means in terms of efficiency.
A Proficient and secure way of Transmission using Cryptography and Steganography

People are concerned about the security of their data over the internet. The data can be protected in many ways to keep unauthorized individuals from accessing it. To secure data, steganography can be used in conjunction with cryptography. It is common for steganography to be used for hiding data or secret messages, whereas cryptography encrypts the messages so that they cannot be read. As a result, the proposed system combines both cryptography and steganography. A steganographic message can be concealed from prying eyes by using an image as a carrier of data. In steganography, writing is done secretly or covertly. The digital steganography algorithm uses text, graphics, and audio as cover media. Due to recent advancements in technology, steganography is challenging to employ to safeguard private data, messages, or digital photographs. This paper presents a new steganography strategy for confidential communications between private parties. A transformation of the ciphertext into an image system is also performed during this process. To implement XOR and ECC (Elliptic Curve Cryptography) encryption, three secure mechanisms were constructed using the least significant bit (LSB). In order to ensure a secure data transmission over web applications, both steganography and cryptography must be used in conjunction. Combined techniques can be used and replace the current security techniques, since there has been an incredible growth in security and awareness among individuals, groups, agencies, and government institutions.
Lightweight Deep Learning Model for Traffic Light Detection

Traffic Light Detection is one among the vital applications of Advanced Driving Assistant Systems (ADAS). The objective of traffic light detection is classification and localization of traffic lights present in the real-time images. State of art research outlines that the deep learning models have achieved better results than the traditional methods in detecting the traffic lights but still the techniques suffer from problems of low accuracy, slow speed, small object detection, illumination variations and occluded objects. In this paper Darknet19 feature extractor of You Only Look Once (YOLO) version 2 is replaced with SqueezeNet pretrained model. The. To improve the detection results, K-means clustering algorithm is applied for finding the clusters of bounding boxes of subset of LaRA traffic light dataset that comprises of green and red traffic lights and seven different sized anchor boxes were chosen for experimentation. Data augmentation techniques are also applied for increasing the diversity of the dataset. The proposed model attained mean average precision (mAP@.50) score of 84% and the visual results on the test dataset showed that the traffic lights were detected with increased confidence score with the proposed model in comparison with MobileNetV2, ResNet50 as backbones of the original YOLOv2 model.
An Original Approach to Identify the Better Accuracy in Credit Card Fraud Transaction by Comparing Logistic Regression with K-Nearest Neighbours Algorithm

This work was extracted from the raw data by suggesting machine learning techniques like the clustering model K-Nearest Neural Network (KNN) to determine whether the input data classifies legitimate or fraudulent transactions and to obtain greater accuracy. Materials and Methods: Detect the fraud techniques utilizing the F1-Measure score is the mean between precision and recall. The range of F1 scores is [0,1]. Implementing Machine learning algorithms, the sample size for Logistic Regression (N = 20), proposing the under-sampling technique is K-NN (N = 20) and G power (value = 0.8). Results: K-Nearest Neural Network method classifies any new incoming transaction calculating the K-Nearest point to achieve this accuracy. The independent sample T-Test (=.272) result (p0.05) with a 95% confidence level does not statistically support the two algorithms KNN and LR. Conclusion: K Nearest Neural Algorithm proves (99.4%) to detect fraud, false alert rate with accuracy it appears to be better than Logistic Regression (99.1%).
Hybrid Framework for Sentiment Analysis Using ConvBiLSTM and BERT

Sentiment analysis is specifically a text mining technique that utilizes natural language processing to computerize the process of analyzing text that aims to determine the sentiment expressed. The fundamental purpose of sentiment analysis is to get valuable insights that lead to all-around development in specific domains. The fantastic applications of sentimental analysis include monitoring social media, management of customer support, and customer reviews research. One of the major pitfalls in sentiment analysis is word ambiguity. To overcome this drawback, a proposed hybrid framework presented in this work is capable of dealing with such ambiguity issues. The considered evaluation parameters are accuracy, F1 score and time taken. The proposed hybrid framework utilizes Convolutional Bi-directional Long short-term memory network (ConvBiLSTM) with Bidirectional Encoder representations from Transformer (BERT) tokeniser on the given dataset and outperform other methodologies with 95.10% accuracy.
The Quantum and Classical Streaming Complexity of Quantum and Classical Max-Cut

We investigate the space complexity of two graph streaming problems: MAX-CUT and its quantum analogue, QUANTUM MAX-CUT. Previous work by Kapralov and Krachun [STOC 19] resolved the classical complexity of the classical problem, showing that any (2 – ε)-approximation requires Ω(n) space (a 2-approximation is trivial with O(log n) space). We generalize both of these qualifiers, demonstrating Ω(n) space lower bounds for (2 – ε)-approximating MAX-CUT and QUANTUM MAX-CUT, even if the algorithm is allowed to maintain a quantum state. As the trivial approximation algorithm for QUANTUM MAX-CUT only gives a 4-approximation, we show tightness with an algorithm that returns a (2 + ε)-approximation to the QUANTUM MAX-CUT value of a graph in O(log n) space. Our work resolves the quantum and classical approximability of quantum and classical Max-Cut using o(n) space.We prove our lower bounds through the techniques of Boolean Fourier analysis. We give the first application of these methods to sequential one-way quantum communication, in which each player receives a quantum message from the previous player, and can then perform arbitrary quantum operations on it before sending it to the next. To this end, we show how Fourier-analytic techniques may be used to understand the application of a quantum channel.
Research on voiceprint identity recognition and verification technology and its application in mobile payment

This paper studies an identity recognition and verification system based on the DTW voiceprint recognition algorithm. The system hardware is composed of a TC264 single-chip control chip, and the MCP6022 microphone module is used as a sensor. Through AD conversion, the input voice is converted into an arithmetic matrix, and the MEL frequency is used. Cepstral coefficient and MFCC algorithm perform spectrum analysis on the voiceprints of different people, upload them to the data cloud to form a voiceprint database, and finally complete the synchronous transmission of the data through communication with the mobile phone software platform, and complete the identification and verification of the identity on the mobile phone.