Reviewerly/exHarmony huggingface dataset:
Simulation of Highly Coupled Programmable Photonic Circuits

Multipurpose programmable photonic processors have recently emerged as a solution to provide cost-effective and general-purpose functionality for a myriad of photonic applications. Inspired by programmable electronics, the design of these devices lies on generic photonic integrated hardware based on two-dimensional waveguide meshes comprised of tunable couplers and phase actuators. Extending these meshes allows the implementation of more complex structures and functionalities. However, there are design trade-offs arising from parasitic effects coming from fabrication and their dynamic operation. Since the time spent during the design cycle and validation of these complex systems usually involves a costly, risky and time-consuming processes, this work proposes and compares two simulation tools to predict the spectral response of any 2D integrated photonic mesh circuit composed of an arbitrary number of coupled cells. These methods reduce development costs, speed up the growth of new circuit designs, and are a fundamental tool for the development of programmable photonic libraries.
Medical Ultrasound Image Segmentation Using U-Net Architecture

This research article discusses the implementation aspects of a Deep Learning architecture based on U-Net for medical image segmentation. A base model of the U-Net architecture is extended and experimented. Unlike the existing model, the input images are enhanced by applying a Non-Local Means filter optimized using a metaheuristic Grey wolf optimization method. Further, the model parameters are modified to achieve better performance. Tests were performed using two benchmark B-mode Ultrasound image datasets of 200 Breast lesion images and 504 Skeletal images. Experimental results demonstrate that the modifications resulted in more accurate segmentation. The performance of the modified implementation is compared with the base model and a Bidirectional Convolutional LSTM architecture.
Engine vibration anomaly detection in vessel engine room

Unsupervised and autonomous operations require new, more efficient, and smarter technical solutions. Digitalization is one of the enablers of autonomous operation, with installing new sensors and using the data for AI and machine learning. The trend is to move the handling the vast amount of data from sensors, installed in machines, and devices, close to the sensors. This way, data communication can be heavily reduced, while the latency of decisions based on data can be minimal. This is achieved by moving the advanced data analysis capabilities from the cloud to the edge. In this paper, the driver of technological development is the concept of an unmanned machine room, where the normal inspections performed by personnel are automated using sensors and algorithms.We present an analysis of vibration data, recorded at a cruise ferry\x92s engines, using a signal processing method called cepstrums. A cepstrum is a version of frequency analysis, intending to identify and visualize periodic structures in data using cepstrograms. Compared to spectrogram analysis, usage of cepstrogram enables better visualization of engine behavior into the shape of audio signals. This helped us discover the run-time vibration routine of the engines from where we could observe unusual vibration sensing generated from the engine. We started studying the unusual behavior and came to the conclusion that the engine is sometimes misfiring the cylinders, generating anomalies
Transformation-Based Fuzzy Rule Interpolation With Mahalanobis Distance Measures Supported by Choquet Integral

Fuzzy rule interpolation (FRI) strongly supports 1 approximate inference when a new observation matches no rules, 2 through selecting and subsequently interpolating appropriate 3 rules close to the observation from the given (sparse) rule 4 base.Traditional ways of implementing the critical rule selection 5 process are typically based on the exploitation of Euclidean 6 distances between the observation and rules.It is conceptually 7 straightforward for implementation but applying this distance 8 metric may systematically lead to inferior results because it fails 9 to reflect the variations of the relevance or significance levels 10 amongst different domain features.To address this important 11 issue, a novel transformation-based FRI approach is presented, 12 on the basis of utilising the Mahalanobis distance metric.The 13 new FRI method works by transforming a given sparse rule 14 base into a coordinates system where the distance between 15 instances of the same category becomes closer while that between 16 different categories becomes further apart.In so doing, when an 17 observation is present that matches no rules, the most relevant 18 neighbouring rules to implement the required interpolation are 19 more likely to be selected.Following this, the scale and move 20 factors within the classical transformation-based FRI procedure 21 are also modified by Choquet integral.Systematic experimental 22 investigation over a range of classification problems demonstrates 23 that the proposed approach remarkably outperforms the existing 24 state-of-the-art FRI methods in both accuracy and efficiency.
Blur-Robust Object Detection Using Feature-Level Deblurring via Self-Guided Knowledge Distillation

Images captured from real-world environments often include blur artifacts resulting from camera movement, dynamic object motion, or out-of-focus. Although such blur artifacts are inevitable, most object detection methods do not have special considerations for them; therefore, they may fail to detect objects in blurry images. One possible solution is applying image deblurring prior to object detection. However, this solution is computationally demanding and its performance heavily depends on image deblurring results. In this study, we propose a novel blur-aware object detection framework. First, we construct a synthetic but realistic dataset by applying a diverse set of motion blur kernels to blur-free images. Subsequently, we leverage self-guided knowledge distillation between the teacher and student networks that perform object detection using blur-free and blurry images, respectively. The teacher and student networks share most of their network parameters and jointly learn in a fully-supervised manner. The teacher network provides image features as hints for feature-level deblurring and also renders soft labels for the training of the student network. Guided by the hints and the soft labels from the teacher, the student network learns and expands their knowledge on object detection in blurry images. Experimental results show that the proposed framework improves the robustness of several widely used object detectors against image blurs.
Development of Estimating Algorithm for Biodegradation of Chemicals Using Clustering and Learning Algorithm

Chemical substances should be assessed for biodegradation in environment. The biodegradation tests usually require 28 days of continuous testing and expensive costs. To reduce the cost, some software has been proposed to estimate the biodegradation of chemicals. Although, the software has not enough performance. Therefore, we develop a new estimation algorithm for the biodegradation of chemicals using clustering and machine learning algorithms. The combination of the Birch clustering algorithm and the XGboost learning algorithm is proposed to estimate the biodegradation of chemicals. Using 4200 real tested chemicals, the proposed algorithm was examined. The Birch algorithm might collect chemicals that are clear relationship between the explanatory variables and biodegradation.
Designing Framework for Tabletop Exercise to Promote Resilience Against Cyber Attacks

In recent years, there has been an increase in the number of cyber-attacks targeting ICS (Industrial Control System), also used in the critical infrastructure that supports our daily lives. As a result, companies using ICS need to improve their ability to respond to cyber incidents to ensure business continuity. Cyber incidents at ICS require an enterprise-wide response, as they have a significant impact on IT system security issues, plant safety, and the company's business. For this company-wide response to proceed smoothly, it is essential to create a response plan in advance and create an environment that allows for an instant response when an incident occurs. Many companies do not have enough experience with ICS-targeted incident response to develop this plan. In this study, we developed an exercise to simulate an incident and improve response to a cyber-attack.
Towards Identification of Privacy Requirements with Systems Thinking

Implementing privacy as software functions is required by privacy regulation. Achieving this requires shared understanding between business process owners and software engineers, who implement it. Current literature reveals a major gap between privacy requirements and how engineers interpret privacy. Furthermore, as today’s sociotechnical systems are increasingly complex and ever-evolving, unknown privacy issues can emerge from them as a side-effect. Understanding privacy and identifying privacy threats are pre-requisites for deciding on and implementing the right functionality in software. However, current methods for privacy threat identification do not cover all aspects of privacy, suit complex sociotechnical systems or requirements engineering, or support engineers forming a mental model of privacy. We claim that this situation can be improved by applying a systems thinking approach to privacy threat identification. In this paper, we elaborate the problem and propose a research agenda that will help close the gap between privacy requirements and technical software functionality.
A local dense decision-making method for solving the maximum clique problem in large-scale undirected graphs

The maximum clique problem is a classic difficult problem. Most traditional solutions are branch and bound-based exact algorithms that perform well in terms of both accuracy and time at small legend sizes. However, in recent years, the scale of legend models in practical applications has expanded rapidly, and the traditional undirected graphical model solving methods are no longer applicable. At larger scales, a heuristic decision-making method for local density is proposed, a density index function is constructed, and a decision-making inference for finding maximum cliques is established. Our algorithm avoids random and disorderly traversal solutions, and it ensures the accuracy of the solution process. At the same time, a fast search threshold is added to the algorithm to improve the solution efficiency and local optimization ability. The experimental comparison shows that this algorithm and its improvements have better solution accuracy and solution time compared with the maximum clique accuracy algorithm.
EfficientNet MW: A Mask Wearing Detection Model with Bidirectional Feature Fusion Network

To solve the problem of missing model detection for small targets, occluded targets, and crowded targets scenarios in mask detection, we propose an end-to-end mask-wearing detection model based on a bidirectional feature fusion network. Firstly, to improve the ability of the model to extract features, we introduce the modified EfficientNet as the backbone network in the model. Secondly, for the prediction network, we introduce depth-wise separable convolution to reduce the amount of model parameters. Lastly, to improve the performance of the model on small targets and occluded targets, we propose a bidirectional feature fusion network and introduce a spatial pyramid pooling network. We evaluate our proposed method on a real-world data set. The mean average precision of the model is 87.54%. What’s more, our proposed method achieves better performance than the comparison approaches in most cases.
Local Defogging Algorithm for Improving Visual Impact in Image Based on Multiobjective Optimization

The preprocessing of images is required for many applications based on industry, social, and academic requirements. Researchers have developed a number of techniques to improve the visual effect of images and appropriately interpret visual effects. The accuracy of visuals is important in cyber security, military organization, police organizations, and forensics to detect the true story from the pictures. They search for evidence by digging deep into the network in search of evidence. If visuals are not clear, preprocessing of images is not done correctly, then it may lead to wrong interpretations. This paper proposes an image local defogging technique based on multiobjective optimization to improve the visual effect of the image as well as the information entropy. The multiobjective function is selected to establish the image reconstruction model based on multiple objectives. The model is utilized to reconstruct a single image to moderate the impact of noise and other interference factors in the original image. The color constancy model and effective detail intensity model are also devised for image enhancement to get the visual details. The atmospheric light value and transmittance are evaluated using a physical model of atmospheric scattering, and the guided filter is used to maximize the transmittance of a single image and improve the efficiency of image defogging. The dark channel priority method is used to realize the local defogging of a single image and to design the local defogging algorithm. Experiments verify the optimization effect of the proposed algorithm in terms of information entropy and container network interface (CNI) value. The tone restoration degree is good, and it improves the overall image quality. The image defogging effect of the proposed algorithm is verified with respect to subjective and objective levels to check the efficacy of the proposed multiobjective model.
Importance Estimation for Scene Texts Using Visual Features

In this study, we addressed the challenge of estimating the importance of texts in scene images. Research on text analysis in scene images has focused on detection and recognition; however, estimating its importance has not received much attention. We focused on the possibility that importance can be estimated from visual appearance. Therefore, in this study, we constructed scene image datasets, including texts, and assigned an importance to each text via subjective evaluation. Based on the subjective evaluation, the image features representing importance of text contents were determined, and an importance estimation model is proposed. The results of the evaluation experiment indicate that the proposed method can estimate the importance with a higher accuracy than the existing method.
Research on Education Teaching Quality Analysis Based on the Neural Network Model

Universities emphasize quality review and monitoring. To effectively evaluate classroom teaching effectiveness, a trustworthy model of teaching quality evaluation is required. Due to the fact that teaching is a dynamic process, numerous elements influence teaching quality, and the link between assessment index and teaching effect is complex and nonlinear. There are numerous methods for measuring the quality of classroom instruction, but the vast majority of it relies on a single machine learning algorithm, making it difficult to construct an accurate and reliable mathematical model. In this paper, we employ the AdaBoost’s multicore neural network learning algorithm to learn several weak classifiers and combine them into a single strong classifier. We also transfer the classification probabilities into teaching quality outcomes to obtain the final teaching quality results. Our model offers a new, effective way for evaluating the quality of classroom instruction, and it can serve as a solid theoretical resource for reforming classroom instruction.
Smart Teaching Design Mode based on Machine Learning and its Effect Evaluation

With the continuous progress of science and technology, the mode of smart teaching is more and more applied in the actual teaching process. Under the intelligent teaching system mode, teachers and students can jointly complete the construction of curriculum resources and classroom teaching tasks. This paper evaluates its application effect by introducing two examples of smart teaching. Among them, the evaluation effect of the academic teaching network system shows that the teaching network system based on machine learning is composed of three parts: model selection, data preparation, and modeling prediction. Moreover, it can greatly improve the prediction performance of the intelligent algorithm in the teaching mode by introducing the oversampling technology SMOTE algorithm (Synthetic Minority Oversampling Technique). To further verify the advantages of the smart teaching model, an example of social sciences teaching is introduced. The results show that the relevant predictive performance indicators in the computer engineering discipline are improved by using a combination of intelligent algorithms.
A fisher score-based multi-instance learning method assisted by mixture of factor analysis

Multi-instance learning (MIL) is a problem with significant importance due to the existence of ambiguous labels in many practical tasks. The training data in MIL problem is represented by labeled bags containing a various number of unlabeled instances. This inexact label information has brought complexities to the classification of instances and bags. Fisher scores have been utilized to extract fixed-length representation vectors from the MIL bags with the assistance of generative models. However, the generative model utilized by the existing methods might disregard the covariance information. In this paper, we propose a multi-instance learning method (miMFA) utilizing Fisher scores derived from the mixture of factor analysis (MFA) model. The MFA generates nonlinear data with a set of local factor analysis models, while each local model approximates the full covariance Gaussian using latent factors. Thus, the MFA could cover the data distribution and generate Fisher scores effectively. The MFA-based Fisher score is then utilized to form the bag representation. Moreover, a reinforcement vector and the complementary information from auxiliary MIL methods are introduced for performance refinement. The proposed miMFA is evaluated on 11 MIL datasets, and achieves satisfactory results compared with the existing methods.
Improving the Security of Video Embedding Using the CFP-SPE Method

With the amount of data being transferred on a daily basis, it is becoming increasingly dangerous to save data on the Internet in the face of intruders or hackers. This study paper is one of the most effective ways to transmit information in a secure and confidential manner. The authors previously disclosed a way for embedding a secret video inside a cover video in their prior work. The writers have implemented a number of techniques to incorporate the secret video. The current work improves on the existing approach by including encryption and decryption concepts into the video embedding process. The secret data for either a large or little amount of information is put on the cover video utilising the embedding technique. Our proposed method combines compression, encryption, decryption, and secret information embedding to provide a more secure data transfer.
Privacy invasion via smart-home hub in personal area networks

Smart-home devices are being increasingly used in our daily lives. While these devices provide convenient functions to users, such convenience may come at a greater cost, such as the leakage of the user's private information. This paper presents a system ChatterHub to address privacy risks in smart-home devices. Specifically, this work focuses on the devices that use Zigbee or Z-wave and are controlled by a centralized smart-home hub in a personal area network (PAN) for connecting to the Internet. ChatterHub passively eavesdrops on encrypted network traffic from the hub and leverages machine learning techniques to classify events and states of smart-home devices. We deployed ChatterHub on three real-world smart-home settings to evaluate its accuracy and efficiency. The evaluation results show that the attacker can successfully disclose smart-home devices' behaviors with over 89% of recall and F1-score. We also demonstrate that an attacker can interfere with the smart-home hub's communication and selectively drop packets to disable alerting users of a device's status, such as security sensors and smart-locks. Furthermore, as a mitigation approach, we developed a packet-injection approach to effectively prevent threats from ChatterHub by generating only 9.2 MB of extra network traffic per day.
Deep Learning Scoring Model in the Evaluation of Oral English Teaching

This study is aimed at improving the accuracy of oral English recognition and proposing evaluation measures with better performance. This work is based on related theories such as deep learning, speech recognition, and oral English practice. As the literature summarized, the recurrent neural network was the calculation standard, and the oral English speech recognition indicators were the main basis on which an English speech recognition model was constructed. Then, 20 English majors and 5 sets of English sentence patterns were randomly selected as the research objects. The correction standards for English oral errors were introduced into the model to achieve further improvement. The research results showed that the average concordance rate of speech recognition reached 91% through the model test. The concordance rates of words, speech, and intonation in recognition were 89%, 91%, and 86%, respectively. The model could be used as an evaluation system for English speech recognition. Therefore, the application of the deep learning scoring model in the evaluation of oral English teaching was researched in this work, which provided an effective basis for the evaluation of intelligent English teaching.
Sentiment Analysis and Personalized Recommendations Based on JD.com Reviews

The general big data personalized recommendation is based on the number of times or the length of time users click on a related content, but in many cases, these cannot be the most direct basis for accurate recommendation, and there may be cases such as wrong clicks by users. These factors and a large number of related products or articles recommended to users may cause users' disgust. This article conducts sentiment analysis on JD.com reviews as an example, obtains the user's likes and dislikes, and then makes accurate personalized recommendations, so that a greater understanding of preferences can improve the effect of recommendations, and more accurate personalized recommendations.