Reviewerly/exHarmony huggingface dataset:
Evolution of Web Application Programming Interfaces: Driving Forces, Effects on Clients, and Patterns for Providers

The current state of research on finding effective methods of maintaining compatibility between providers of web application programming interfaces and their clients is described. Reasons and types of changes in the web application programming interfaces, effects of the chan¬ges on the clients, patterns for developers, adaptation methods, and regression testing are reviewed.
Privacy-Preserving Fleet-Wide Learning of Wind Turbine Conditions with Federated Learning

A wealth of data is constantly being collected by manufacturers from their wind turbine fleets. And yet, a lack of data access and sharing impedes exploiting the full potential of the data. Our study presents a privacy-preserving machine learning approach for fleet-wide learning of condition information without sharing any data locally stored on the wind turbines. We show that through federated fleet-wide learning, turbines with little or no representative training data can benefit from accuracy gains from improved normal behavior models. Customizing the global federated model to individual turbines yields the highest fault detection accuracy in cases where the monitored target variable is distributed heterogeneously across the fleet. We demonstrate this for bearing temperatures, a target variable whose normal behavior can vary widely depending on the turbine. We show that no member of the fleet is affected by a degradation in model accuracy by participating in the collaborative learning procedure, resulting in superior performance of the federated learning strategy in our case studies. Distributed learning increases the normal behavior model training times by about a factor of ten due to increased communication overhead and slower model convergence.
Online Multiple Object Tracking Using Min-Cost Flow on Temporal Window for Autonomous Driving

Multiple object tracking (MOT), as a core technology for environment perception in autonomous driving, has attracted attention from researchers. Combing the advantages of batch global optimization, we present a novel online MOT framework for autonomous driving, consisting of feature extraction and data association on a temporal window. In the feature extraction stage, we design a three-channel appearance feature extraction network based on metric learning by using ResNet50 as the backbone network and the triplet loss function and employ a Kalman Filter with a constant acceleration motion model to optimize and predict the object bounding box information, so as to obtain reliable and discriminative object representation features. For data association, to reduce the ID switches, the min-cost flow of global association is introduced within the temporal window composed of consecutive multi-frame images. The trajectories within the temporal window are divided into two categories, active trajectories and inactive trajectories, and the appearance, motion affinities between each category of trajectories, and detections are calculated, respectively. Based on this, a sparse affinity network is constructed, and the data association is achieved using the min-cost flow problem of the network. Qualitative experimental results on KITTI MOT public benchmark dataset and real-world campus scenario sequences validate the effectiveness and robustness of our method. Compared with the homogeneous, vision-based MOT methods, quantitative experimental results demonstrate that our method has competitive advantages in terms of higher order tracking accuracy, association accuracy, and ID switches.
Application of SVM and Chi-Square Feature Selection for Sentiment Analysis of Indonesia’s National Health Insurance Mobile Application

(1) Background: sentiment analysis is a computational technique employed to discern individuals opinions, attitudes, emotions, and intentions concerning a subject by analyzing reviews. Machine learning-based sentiment analysis methods, such as Support Vector Machine (SVM) classification, have proven effective in opinion classification. Feature selection methods have been employed to enhance model performance and efficiency, with the Chi-Square method being a commonly used technique; (2) Methods: this study analyzes user reviews of Indonesia’s National Health Insurance (Mobile JKN) application, evaluating model performance and identifying optimal hyperparameters using the F1-Score metric. Sentiment analysis is conducted using a combined approach of SVM classification and Chi-Square feature selection; (3) Results: the sentiment analysis of user reviews for the Mobile JKN application reveals a predominant tendency towards positive reviews. The best model performance is achieved with an F1-Score of 96.82%, employing hyperparameters where C is set to 10 and a “linear” kernel; (4) Conclusions: this study highlights the effectiveness of SVM classification and the significance of Chi-Square feature selection in sentiment analysis. The findings offer valuable insights into users’ sentiments regarding the Mobile JKN application, contributing to the improvement of user experience and advancing the field of sentiment analysis.
Anonymization Procedures for Tabular Data: An Explanatory Technical and Legal Synthesis

In the European Union, Data Controllers and Data Processors, who work with personal data, have to comply with the General Data Protection Regulation and other applicable laws. This affects the storing and processing of personal data. But some data processing in data mining or statistical analyses does not require any personal reference to the data. Thus, personal context can be removed. For these use cases, to comply with applicable laws, any existing personal information has to be removed by applying the so-called anonymization. However, anonymization should maintain data utility. Therefore, the concept of anonymization is a double-edged sword with an intrinsic trade-off: privacy enforcement vs. utility preservation. The former might not be entirely guaranteed when anonymized data are published as Open Data. In theory and practice, there exist diverse approaches to conduct and score anonymization. This explanatory synthesis discusses the technical perspectives on the anonymization of tabular data with a special emphasis on the European Union’s legal base. The studied methods for conducting anonymization, and scoring the anonymization procedure and the resulting anonymity are explained in unifying terminology. The examined methods and scores cover both categorical and numerical data. The examined scores involve data utility, information preservation, and privacy models. In practice-relevant examples, methods and scores are experimentally tested on records from the UCI Machine Learning Repository’s “Census Income (Adult)” dataset.
An Efficient Voice Authentication Approach Using Improved Deep Convolutional Neural Networks with LSTM Networks

AbstractVoice authentication and verification play an essential role in forensic science and security systems in today's technological environment. A challenging issue is making precise voice authentication and verification. This document aims to identify scammers using machine learning techniques to find the best speaker identification and classification combination. Traditionally, researchers have approached voice authentication and speech comparison using separate classification and comparison models. However, the accuracy of training with large amounts of data has made deep learning more popular. In this study, we predict speech recognition in speech datasets using an improved deep convolutional neural network (IDCNN) and long short-term memory (LSTM) network structure. The proposed approach entails three main steps: pretreatment, feature extraction, and classification. We could predict voice authentication and verification in this study with an accuracy of 99.3%. Aside from comparing classification performance, performance metrics for accuracy, sensitivity, and specificity were also computed. The newly introduced methods offer superior error rates, precision, F-score accuracy, and recall compared to conventional algorithms. This research is regarded as a pioneer in the field as a quick and effective way to enhance continuous speech recognition activity.KEYWORDS: Improved DCNN (IDCNN)Long short term memory (LSTM)VGG-16 and CNN Disclosure statementNo potential conflict of interest was reported by the author(s).Additional informationNotes on contributorsN. KaladharanN Kaladharan received his under graduate in electrical and electronics engineering in 2004 and obtained his masters in computer science and engineering 2009 from Annamalai University. He is doing his research in speech processing. Currently, he is a lecturer of department of computer engineering at Government Polytechnic College, Kottur - Theni. His research interests are speech processing, machine learning areas in the academic environments. Corresponding author. Email: nkeeeau@gmail.com.R. ArunkumarR Arunkumar received his under graduate in electronics and communication engineering from AVC college of Engineering, Tamilnadu in 2000 and obtained his masters in computer science and engineering 2005 from Annamalai University. He received his PhD in computer science and engineering from Annamalai University in 2016. He has published many technical papers in national and international conferences and journals. Currently, he is an associate professor of computer science and engineering, faculty of Engineering and Technology, Annamalai University, Tamil Nadu, India. He is an associate editor and reviewer for several national and international journals. His research interests include image processing, machine learning, data and computer networks, information coding techniques, internet of things. Email: arunkumar_an@yahoo.com
The development and application of virtual practical teaching based on a clustering algorithm in the development and application of a resource library of college Civics courses

Abstract With the ever-changing times and the expanding scale of colleges and universities, the contradiction between the scarcity of Civics course resources and the unlimited demand of students for resources in our colleges and universities has become more obvious. It is of great practical significance to develop Civics course resources for the high-quality development of colleges and universities. In this paper, we use the clustering algorithm to reduce the complexity of the resource allocation algorithm and the ant colony algorithm to solve the allocation problem between the resource user group and distributed distribution center and then combine the two algorithms to optimize the college Civics course resource library system. The research shows that the retrieval time of the other two algorithms is 247s and 251s, respectively, while the retrieval time of this algorithm is 148s, which is much faster than the other two algorithms, and the slowest query concurrency can reach 3.61 per second under the condition of 20 users, which meets the acceptable query requirements of users. Finally, after teaching tests, students are generally satisfied with the teaching resources organized and managed in the teaching aid platform in terms of richness, quality, and conformity with learning needs. Therefore, the college Civics course resource library system in virtual practice teaching based on the clustering algorithm designed in this paper can manage the course resources well and have a good retrieval effect, which can also play a multiplier effect in Civics teaching.
Low Code Backend As A Service Platform

As software development becomes increasingly complex, code generators have emerged as a promising solution freeing up developers to focus on more complex tasks. The challenge becomes acute in the case of web development where the server-side logic is crucial for responsiveness to the users. This paper focuses on providing a low code Backend as a Service for basic functionalities such as database management and user authentication so as to ease the process of development of backend code in terms of time, effort and expertise required. The services are provided as dockers so as to increase the scope of deployment and efficiency of the service/application.
Deep Learning Approach for Identification of Fake Profiles in Social Media

Fake profiles have been developed as social media usage has increased, which can result in identity theft, cyberbullying, and online fraud. In order to secure internet users, efficient phony profile detection systems are required. In this study, we assess the efficacy of three well-known machine-learning algorithms and deep learning methods for the detection of fake profiles such as Support Vector Machines (SVM), Random Forest, and Neural Networks.The dataset aided for training and testing includes several variables taken from social media profiles, such as status_ count, followers count, friends count, favorites count, and listed count. The testing results demonstrate that all three algorithms are capable of identifying phony profiles with a high degree of accuracy, with neural networks having the best accuracy (99.2%). This study implies that machine learning algorithms have the potential to identify fraudulent profiles.
Invasive Ductal Carcinoma Prediction in Mammography and Histopathology Images Using CNN

This study investigates the application of a deep learning model to the diagnosis of invasive ductal carcinoma (IDC) from mammography and histopathology images. The ultimate goal is to improve early detection and treatment of breast cancer and reduce mortality. The study used the InceptionResNetV2 model for the mammography dataset, which produced an impressive accuracy of 95.84%. A standard Convolutional Neural Network (CNN) model was used for the histopathology dataset, with a ReLU function, 128 neurons, and a Softmax layer with 2 neurons and this model achieved an accuracy of 96.23%. These results demonstrate the ability of deep learning models to accurately predict IDC from medical imaging data, which has the potential to improve patient outcomes by enabling earlier diagnosis and treatment. By emphasizing the effectiveness of these models, this review contributes to the development of more accurate diagnostic tools and breast cancer treatment strategies.
Indian Sign Language (ISL) Recognition And Translation using Mediapipe and LSTM

A person having hearing-impairment is always difficult to communicate with. Without a doubt, sign language has become the finest alternative and a very powerful tool for those who have difficulty speaking or hearing to communicate their opinions and emotions to others. It makes the process of integrating them into society easier and more convenient. The signs usually get jumbled and incomprehensible for someone who has never studied sign language or perceives it in another language. The goal is to develop a method that can enable two-way communication between hearing-impaired persons and others and to break down the communication barrier among those who don’t comprehend sign language and persons with impairments. The objective of this research is to demonstrate and create a method for understanding Indian sign language that combines MediaPipe for hand landmark extraction, LSTM for gesture training and recognition, and also captures either English or Hindi audio through the microphone and recognize it using Google Speech API or convert entered text into their relevant Indian sign language videos.
Multimodal Fusion based Adult Website Detection

The internet is a vast source of information, but it also contains a significant amount of adult content that is not appropriate for children, schools and office environments. The problem of such content on the internet is addressed by utilizing deep learning models to identify and block Not Safe For Work(NSFW) websites. Current firewalls lack in detecting websites with only images, text or misleading domain names. This proposed solution uses a multimodal weighted late fusion technique, combining features of the website’s source code and images, to classify a website as safe or NSFW. Firewalls can use the proposed solution to effectively block NSFW content to provide internet security and maintain a healthy and productive environment.
Implementation of Discrete HMMs for Isolated Spoken Word Recognition

Isolated spoken words are acoustically separated from other spoken words by moments of silence. In this work, we have implemented Discrete Hidden Markov Models (DHMMs) for Isolated Spoken Words Recognition. Discrete HMMs have been successfully used in many temporal pattern recognition problems like speech recognition, handwriting recognition, gesture recognition, etc., for more than four decades. We have implemented a basic HMM from scratch in Java language and applied it to train and recognize isolated spoken words. We have tested our implementation in recognizing ten digits on a custom built speaker database. We have obtained an accuracy of 95 % and 82.5% for training and testing respectively.
SLOTSA: A Multi-Strategy Improved Tunicate Swarm Algorithm for Engineering Constrained Optimization Problems

With the increasing demands on industrial products, the design and manufacturing problems of industrial products are becoming more and more complex. Many industrial design problems belong to nonlinear optimization problems and NP-hard problems, such as shop scheduling, path planning, and industrial part design problems. In the last decade, more and more researchers have developed heuristic- based algorithms to deal with industrial design problems. Tunicate swarm algorithm (TSA) is a newly proposed high performance heuristic optimization algorithm. The TSA algorithm has the problems of low solution accuracy, slow convergence and easy attraction by local extrema. In order to solve some drawbacks of TSA, this paper improves TSA and proposes the Sine cosine -Levy-Opposition-based Tunicate swarm algorithm (SLOTSA) by combining opposition-based learning, Levy flight and positive cosine operator. Comparative experiments on SLOTSA in 10 benchmark functions fully demonstrate the rationality of the three improvement strategies. In addition, this paper applies SLOTSA to two practical industrial design problems, and the optimization results demonstrate the wide applicability of SLOTSA to industrial design problems.
An Integrated Waveguide Resonator Squeezer for Optical Networks

Squeezed light is the fundamental building block of continuous variable quantum optics, the usefulness of which has recently been highlighted in multiple experiments such as boson sampling [1] and gravitational-wave detection [2]. Due to their usefulness, there has naturally been a push to develop integrated sources of these quantum states of light.
Exploiting Kerr Nonlinearity for Photonic Extreme Learning Machines

Extreme Learning Machines (ELM) are feedforward neural networks where most of the connections are randomly fixed, and only the output weights are trained [1]. On small scales, ELMs perform comparably to fully trained networks. Due to their low complexity, ELMs are particularly interesting in scenarios where compact, low-cost and non-electronic alternatives to the von-Neumann architecture are desirable, like in edge-computing [2].
Quantum Interference with Single Molecules: Steps Towards a Competitive Single-Photon Source

Single molecules of polyaromatic hydrocarbons (PAH) in suitable host matrices are known for emitting with high quantum efficiency in very narrow and stable zero-phonon lines (ZPL) [1]. For our experiments we used dibenzotherrylene (DBT) molecule inserted as impurity in anthracene (Ac) nanocrystals, dispersed on a gold substrate and cooled down to 3 K in a closed-cycle cryostat. DBT in Ac features a single-photon dipole-allowed transition around 784 nm. Each molecule is namely identical but environmental conditions like local trapped charges or crystal strain can shift the frequency of its transition in a range of approximately 1 nm [2]. Those static differences alongside any instantaneous interaction with the environment can degrade the ability of the emitted photons to undergo two-photon interference (TPI).
Detection-Loss Tolerant Real-Time Amplitude Measurement for Ultra-Fast Optical Quantum Processors: 5-dB Squeezing from DC to 43 GHz

Measurement-based optical quantum computers (MBOQCs) using continuous-variables (CVs) of light are a candidate for large-scale fault-tolerant quantum computing [1]. This form of processing has a high carrier frequency of light, so it can also perform ultra-fast quantum computing. To achieve ultra-fast MBOQCs, broadband squeezed light generation and ultra-fast realtime amplitude measurement are required. So far, over-4.5-dB squeezed light generation, which is required for preparing a two-dimensional cluster state, has been demonstrated in THz bandwidth by using noise intensity measurement with a narrow bandwidth [2], [3]. However, the realtime amplitude measurement frequency is limited to several GHz. Up to now, the highest frequency is still limited to 9 GHz with 0.1-dB squeezing even though specific detectors have been designed [4]. This is because there is a tradeoff relationship between the bandwidth and the quantum efficiency of homodyne detectors. As a result, we cannot use a fast balanced homodyne detector such as a detector for high-speed optical communications with a large optical loss, which collapses the quantum states of light. To achieve ultra-fast realtime amplitude measurement, we need a new measurement method to overcome the tradeoff limitations.
Efficient Implementation of Time-Bin BB84-QKD Protocol with Phase-Randomized Weak Coherent States

Quantum key distribution (QKD) allows to share symmetric encryption keys between two remote parties, in an unconditionally secure way. Most of the QKD protocols rely on the usage of single photons, which however are difficult to produce in a practical way. The introduction of the decoy-method [1] allowed the usage of attenuated laser pulses whilst demonstrating unconditionally security. Since each qubit needs to carry a random global phase, this protocol relies on phase-randomized coherent states [2] which are normally produced exploiting gain switching mode [3].
Generative Adversarial Learning Boosted by a Photonic Quantum Frequency Coprocessor

Quantum generative learning is a promising candidate to demonstrate practical quantum advantage on state-of-the-art quantum information processing devices in the near future. In particular, photonic quantum frequency coprocessors (QFPs) [1] leverage quantum-correlated light sources, a high degree of mode scalability, robustness to decoherence and integration with preexisting telecom infrastructure. As was demonstrated experimentally in previous work [2], phase control and deterministic frequency mixing allow to manipulate individual frequency modes and provide coherent control of tens of frequency modes for two photons.
Re-parameterized Global Channel Interaction Attention Module for Convolutional Neural Networks

In this paper, a compact and effective attention module for convolutional neural networks (CNNs), called re-parameterized global channel interaction attention module (GCIAM), is presented to achieve better communication of channels. Firstly, we propose the global channel receptive filed (GCRF) to measure the number of adjacent channels participating in inter-channel interactions. Secondly, we adopt multi-scale 1-D convolutions to achieve better coverage of the GCRF. Specifically, our method aggregates the information along channel and then encodes cross-channel relationships via multi-scale 1-D convolutions consist of large kernels and small kernels, which enables easier access to different scales of interactions from a large GCRF and significantly reduces the model complexity. Finally, we develop a structural re-parameterization technique for multi-scale 1-D convolutions to boost the inference speed. Extensive experiments on standard benchmarks verify the superiority of our proposed method over various state-of-the-art (SOTA) counterparts. Our method stably outperforms other attention mechanisms by a large margin at almost no costs.