Reviewerly/exHarmony huggingface dataset:
High Security Optical Metropolitan Area Network based on Advanced Modulation Techniques

We present an application scenario of a Passive Optical Metropolitan Area Network (PO-MAN). Multilevel Polarization Shift Keying (M-PolSK) is used to obtain 40 and 100 Gbps per single channel. Moreover, M-PolSKs allow to implement advanced protection techniques based on birefringence, an intrinsic electro-optical effect of optical fiber usually considered a constraint [1]. Nevertheless, we can exploit this effect to our advantage to protect the information. Unlike spread-spectrum technique in which information is protected by exploiting noise, this new paradigm exploits the birefringence effects specific of the optical fibers. These technological solutions are able to replace or coexist with the current ones and those under development, ensuring backward compatibility and flexibility of the networks.
Tri-Branch GAN: A Semi-supervised Method Based on Rebalance

For deep learning applications in industrial scenarios, few-shot and imbalanced available datasets are very common. Traditional methods usually adopt the idea of hierarchical training, semi-supervised learning and the rebalancing method to train the model respectively, which has certain limitations: Separate trainings does not fully exploit the correlation between the two problems and causes additional computational overhead. Therefore, this paper proposes a semi-supervised learning method based on rebalance, named as Tri-branch GAN (Generative Adversarial Networks). This method makes full use of the correlation between the two problems, avoids the updating coating problem after the model parameter training, and saves the computational cost. Simulation results show that the proposed method can effectively improve the classification accuracy.
Analysis and Prediction of Location based Criminal Behaviors Through Machine Learning

Criminal Behaviors may be predicted, identified, and prevented via data mining. The research of criminal activity and its features is known as criminology. To be more accurate, crime analysis entails investigating and detecting crimes and their connections to convict. Because of the vast number of criminal record and intricacy of the linkages between different types of data, criminology is an excellent place to use data mining techniques. The first stage in doing more research is to identify criminal traits. This study offered an overview of current work in the field of crime prevention or prediction using data mining and machine learning technologies. For the system to learn, a year's worth of crime data is fed into it from a reliable Indian internet source that focuses on murder, kidnapping and abduction as well as dacoits and burglars and rape. To anticipate crime rates in different states, a regression model is created using Indian statistics, which provides data on a wide range of crimes over the last few years. A variety of Machine Learning techniques, including unsupervised, semi-supervised, and supervised learning, are used to improve the accuracy of crime predictions. Local police stations will benefit from this effort with regards to crime avoidance. The results may help policymakers better understand crime prediction and prevention.
RAMA: Robust Auction Scheme with Multiple Authorities in IoT

With the vigorous development of the Internet of Things (IoT), technologies based on IoT have been widely used in various fields. Among them, the electronic auction technology based on the IoT has been applied. The traditional electronic auction scheme cannot be flexibly applied in IoT scenario and most auction schemes often have only one authority, which has a great burden on operation and low efficiency, resulting in a single-point performance bottleneck. In order to solve above problems, we use a hierarchical multiple authorities system to propose a Robust Auction scheme with Multiple Authorities in IoT (RAMA). Besides, a lightweight range query protocol which can get the highest bidder in electronic auction is adopted to avoid deploying expensive sensors and aggregators in the IoT configuration. Moreover, security of the hierarchical multiple authorities system have been proved through rigorous security analysis, which indicate that the adopted range query protocol not only ensures the security of encrypted bidding but also prevents it from being stolen by malicious adversaries. Finally, we conduct simulation experiments and analyze experimental results to verify the feasibility of the scheme. Specifically, when the number of bidders is 1000, the time-consuming to generate the secret key and the time-consuming to obtain the maximum bidding price are not exceed 0.5s in the simulation experiment.
Automated Blasts and Plasma Cells Counting on Manually Prepared Bone Marrow Aspirate Smears, Closest Deep Learning Pipeline for Generalizability

Abstract Introduction/Objective Medical errors, liability and reduced productivity in Hematopathology labs stem from fatigue, stress and shortage. 5-35% of complete blood count specimens (at least 3 billion tests worldwide annually) compared to 100% of bone marrow aspirates require visual examination using microscopes or digital scanners. The deep learning technologies and growing digital scanners market empower for developing work-fit automated visual examination to assist the Hematopathologist. Methods/Case Report 2.6K+ manually smeared and stained bone marrow aspirate slides were scanned using integrated cameras on microscopes with electronic/mechanical control units. On top of the intracellular and background staining/dyeing variations, new scanning-related variations in exposure, resolution/focus and stitching were noted inter-patches and intra-patches. This led to consensus loss and "carry over" during cells identification. A convolutional autoencoder with skip connection for restoring/normalizing the stain and resolution was developed using data simulating the entire variations and artifacts. Generation of dimensions and ground truths was performed just by drawing on cells borders using pens, this enabled to localize and segment 5K+ cells per day with almost 100% accuracy. To help with labelling, intracellular colors split and quantification were applied. The processed 1.72+ million cells for 14 different WBCs classes/types were used to train a U-net architecture for segmentation, a transfer learning with pre-trained network EfficientNetB0 with ImageNet for classification and YOLO for detection. A previously developed pipeline for RBCs counting was used to select the proper areas for counting on smears. New diverse datasets were used for testing. Results were validated against confirmatory tests including immunohistochemistry and flow cytometry. Results (if a Case Study enter NA) The five-by-two cross validations revealed minor performance fluctuations (+/- 1.05%) with significant averages normalization SSIM, detection mAP, segmentation IoU, and classification F1-score exceeded 0.96, 0.97, 0.98 and 0.97, respectively. This beta prototype finds the proper areas for counting, calculates the M:E ratio, collects and counts the differential. Sufficient qualitative abnormalities assessment needs more work. Conclusion Without normalization and at least 100K cell images for each class, there is no generalizability.
Image Denoising for an Efficient Fake Image Identification

Noise and blur may be removed from a photograph during picture restoration. Erasing camera shaking, radar imaging, and the influence of an image system’s reaction to blurry images is challenging in many instances, including photography. Unwanted signals such as thermal or electrical noise or precipitation or snow in a picture are examples of image noise. The deterioration of the picture might be caused by coding, resolution restriction, transmission noise, object motion, camera shaking, or a combination. A sensor, such as a thermal or electrical signal, or an ambient, such as snow or rain, might cause an image to be blurred. There are several probable causes of picture degradation, including coded pictures, resolution restrictions, transmission noise, object motion, camera shaking, and these factors. Distinguishing between high- and low-frequency components may be accomplished by a process known as "picture decomposition," which entails separating a distorted image into two distinct layers, one for texture and the other for structure, from the LF. The present approach is based on a deep CNN architecture that is very customizable and takes advantage of the frequency characteristics of different sorts of artifacts. The same technique may be used for a wide range of image restoration projects by just altering the architecture. Using a quality improvement network based on residual and recursive learning is recommended to reduce noises with similar frequency characteristics. In order to prevent the network from dying, the authors used residual learning to speed up the training process. The researchers also developed new auxiliary classifiers. The surtax was applied to the outputs of two inception modules, and an auxiliary loss over the same labels was calculated, much as in the prior experiment. Weighted averages are used to calculate the total loss function. Recursive learning may drastically decrease the number of training parameters while maintaining or increasing performance, as seen below. It is claimed that the proposed system is built on a pure version of Inception that does not include any leftover connections. Memory optimization or backpropagation may train it without splitting the copies.
Public Opinion Intelligence Analysis Algorithm of Efficient Course Online Practice Platform based on Message Data Cleaning and Evolutionary Algorithm Mining

It is of great significance to effectively collect the emerging massive Internet information and discover the hotspot public opinion on the Internet. This paper studies the representation and characteristics of network public opinion, and proposes the idea of using network method, combining Hits algorithm and PageRank algorithm to mine public opinion hotspots. Carry out specific research on the collection sources, combine the existing technologies and models of domestic and foreign public opinion collection system research, and carry out systematic analysis and design according to the actual needs of the network public opinion monitoring platform of South China University; Data crawling, using multi-threaded parallel crawling to achieve real-time efficient crawling.
Assignment problem solved by two metaheuristic algorithms ACO and HHO

This study presents two population-based, nature-inspired optimization paradigms, named “Harris Hawks Optimization” HHO and “Ant Colony Optimization” ACO. The inspiration of HHO is the collaborative performance and chasing style of Harris' hawks in nature. Otherwise, ACO is inspired by studying the behaviour of real ants. Those two natural motions were scientifically represented to build optimization algorithms. The performance of HHO and ACO optimizers is checked throughout a comparison based on various test functions and an application of a problem called: Minimizing the cost of assigning personnel to a plant.
FPGA based Fall Detection for Elderly People

Majority of people want to live freely at home. Some activities in our daily lives, such as falling, are prone to accidents. Falls can put people in dangerous situations, including death. This study presents a prototype of a fall detection system based on a FPGA interface with accelerometer and gyroscope. Accelerometer and gyroscope sensors are used to improve the accuracy of fall detection. This system is suitable for use both indoors and outdoors. If someone using this is in a deadly condition and need assistance, an automatic call will be sent to family members as an alert. In case of patients, alert will be sent to nearby hospitals and ambulance service. This study can also discriminate between people's physical conditions and their daily activities. The experiment has been tested carefully and can be applied to real applications for the elderly people. In order to monitor the data and do the necessary action in an automated manner the sensor data is sent to the cloud storage through IOT. Once threshold conditions based on the fall types are reached, the alerting action takes place with the help of React functions and the type of react function created in the cloud platform. The data transmitted by the sensor can be viewed by the doctors in order to frame an analysis to prevent the fall in near future by improving the factors where the people rely on to maintain the balanced position. Thus, in this paper the ultimate aim is to measure the tilt angle of the sensor to measure the human posture and detect whether the person has encountered a fall or not and sending the medical assistance by a SMS alert to a nearby hospital and ambulance control room.
SaaS Service Architecture of Online System Platform for Engineering Project Management Teaching based on Computer Data Monitoring Algorithm

This paper proposes a data layer architecture based on mass customization for SaaS software services, and analyzes and compares three data architecture design schemes. A dynamic data expansion model is proposed for the engineering project management teaching course and shared Schema structure. The composition structure and functional modules of the computer basic course teaching resource library are expounded, and the construction and use of the cloud computing-based computer basic course teaching resource library are analyzed with effective ways. Manage the data access of the open SaaS service platform and conduct simulation analysis. The combination of open SaaS service architecture and LRU-RSIZE algorithm can effectively solve the problem of on-demand storage and management of user data.
Underwater Image Enhancement Based on Improved Haze-lines Prior and Histogram Distribution Prior

In order to solve the low contrast and color shift suffered by underwater images, a hybrid underwater image enhancement method is proposed in this paper. The global attenuation ratio of the underwater image is calculated by using the background light. Based on this, the transmission map of the underwater image is estimated by using haze-lines prior. Then the clear underwater image is recovered from the degraded underwater image. A contrast enhancement method is introduced further to enrich the color and details. The experimental results on different real-world underwater scenes indicate that the proposed method produces visually satisfactory results.
Intelligent Interactive Platform Design of Internal Medicine Nursing Courses in Universities based on Internet Cloud Information Sharing Algorithm

With the popularity of cloud computing and related services, many users are using the cloud every day, such as pictures, documents, instant messaging, and information and entertainment on web pages, which can all be used on the cloud platform. Users hope to easily access services and the system can provide services stably. In order to explore the teaching practice effect of flipped classroom based on UMU's mobile interactive platform "Internal Nursing", 105 students in 2018 and 93 in 2017 were enrolled in nursing. Students as the experimental group and the control group, the 2018 class students adopted the flipped classroom teaching method based on the UMU mobile platform. In the process of deepening the construction of the internal medicine nursing course, they had in-depth practice and thinking.
Instigating the Sailfish Optimization Algorithm Based on Opposition-Based Learning to Determine the Salient Features From a High-Dimensional Dataset

The aim of this research critique is to propose a hybrid combination of Opposition-Based Learning and Sailfish Optimization strategy to recognize the salient features from a high-dimensional dataset. The Sailfish Optimization is a swarm-based metaheuristics optimization algorithm inspired by the foraging strategy of a group of Sailfish. Sailfish Optimization explores the search space in only one direction, limiting its converging capacity and causing local minima stagnation. Convergence will be optimal if the search space is reconnoitred in both directions, improving classification accuracy. As a result, combining the Opposition-Based Learning and Sailfish Optimization strategies improves SFO’s exploration capability by patrolling the search space in all directions. Sailfish Optimization Algorithm based on Opposition-Based Learning successfully amalgamates the model to global optima at a faster convergence rate and better classification accuracy. The recommended method is tested with six different cancer microarray datasets for two different classifiers: the Support Vector Machine classifier and the K-Nearest Neighbor classifier. From the results obtained, the proposed model aided with Support Vector Machine outperforms the existing Sailfish Optimization with or without K-Nearest Neighbor in terms of convergence capability, classification accuracy, and selection of the most delicate salient features from the dataset.
Automated Breast Ultrasound

Due to the growing number of breast cancer patients, an early diagnosis is important in order to reduce the mortality rate of those affected. Methods such as mammography, DBT, MRI, HHUS or ABUS are used in the detection of breast cancer. The aim of this article is to review the literature showing the basic principle of ABUS and to point out its advantages and disadvantages in relation to conventional methods of breast imaging. ABUS is a relatively new ultrasound method that performs well on patients with dense breast tissue. It reduces operator dependence and provides valuable diagnostic information with multiplanar reconstructions. Using evidence from reliable researches, studies have demonstrated that ABUS has a higher diagnostic accuracy compared to mammography, which remains the primary modality for early diagnosis of breast cancer. Applying ABUS as an adjunct to mammography during the screening test has proven effective and further confirmed the importance of their application in clinical practice. The disadvantage of the combination of ABUS and mammography was that in a large number of studies the specificity was lower compared to mammography itself. Compared to DBT, ABUS has demonstrated to have a higher diagnostic performance, with the exception that it lacks the ability to effectively detect calcifications. Although MRI seem to outperform ABUS, ABUS devices offer a cost-effective and easy to use imaging system, making it the best alternative. The HHUS technique, on the other hand, was perceived by many studies as less painful, with a shorter operative time compared to ABUS. However, the sensitivity and specificity of this screening method continues to remain inferior to ABUS. The use of artificial intelligence is becoming widely used today. As a result, the CAD software has been developed to be applied in conjunction with ABUS in order to improve the detection rate of breast cancer as well as its accuracy. The use of CAD significantly reduced image reading time and improved the overall diagnostic accuracy of ABUS. According to all the presented data, the use of ABUS medical devices in clinical practice continues to grow in importance and with the further development of technology and medicine, its full integration into healthcare systems around the world is expected.
Marine Object Detection Based on Improved YOLOv5

The detection of marine objects plays an important role in the exploration and exploitation of marine resources and other marine economic activities. Traditional detection methods are inefficient and low in accuracy. With the development of deep learning, YOLO series algorithms have been widely used in various applications. In this paper, an improved algorithm based on YOLOv5 for maritime object detection has been proposed. The improved algorithm modifies the backbone and neck structure of YOLOv5 through attention mechanism and BiFPN. Experimental results show that our proposed algorithm has superior performances and in particular it obtains better detection results for small objects compared with the original YOLOv5 algorithm. The index of mAP is improved by 1%, and the recall rate is improved by 2%.
Design of Remote Platform for Higher Vocational Chinese Teaching based on Cloud Networking File Sharing Algorithm

This paper develops a networked location privacy protection system architecture based on cloud services, and an online location privacy protection algorithm based on PageRank and differential privacy. First, the overall framework of the teaching platform system based on J2EE three-layer structure technology is designed. The terminal module, the teacher-side module and the student-side module develop the system function modules, and finally put forward the effective strategies for the cultivation of core literacy in Chinese teaching. The development of such technologies can share excellent teaching resources with multiple parties, realize the upgrading and optimization of digital information-based network office and teaching assistance systems, and provide convenience for higher vocational students in the actual learning process of Chinese subjects. Students' language application ability.
Real-time Measure Analysis of Mathematical Modeling Training Integrating BOPPPS and Stochastic Data Mining Algorithm

The blended teaching effect of online and offline integration is better than the traditional teaching and the teaching effect based on the BOPPPS teaching mode alone, but the BOPPPS teaching mode can promote the integration of online and offline teaching to a certain extent. The mathematical model of our school trains the students' thinking logic, practical problems, model solving, professional writing, through the introduction of the types of digital models, the characteristics of digital models, construction methods, and the training of high vocational students on the construction of digital models, applying data mining algorithms to realize a Tibetan big data analysis platform, and in accordance with software engineering ideas. As well as some problems existing in curriculum setting, competition organization, incentive measures, and system construction in the organization and implementation.